Optimal Versus Robust Inference in Nearly Integrated Non-Gaussian Models

摘要

Elliott,Rothenberg,and Stock (1996,Econometrica 64,813-836) derive a elass of point-optimal unit root tests in a time series model with Gaussian errors.Other authors have proposed "robust" tests that are not optimal for any model but perform well when the error distribution has thiek tails.I derive a elass of point-optimal tests for models with non-Gaussian errors.When the true error distribution is known and has thiek tails,the point-optimal tests are generally more powerful than the tests of Elliott et al.(1996) and also than the robust tests.However,when the true error distribution is unknown and asymmetric,the point-optimal tests can behave very badly.Thus there is a trade-off between robustness to unknown error distributions and optimality with respect to the trend coefficients.